Critical Techniques and Technologies for Advancing Big Data Science Engineering | Apply Online

Opportunity Summary:

The National Science Foundation (NSF) funding opportunity titled "Critical Techniques and Technologies for Advancing Big Data Science and Engineering" (NSF Publication 14-543; Funding Opportunity Number 14-543) is a research-focused solicitation intended to push forward the state of the art in how large-scale, complex data are managed, analyzed, and used to drive scientific discovery. The program is positioned within a broader national Big Data Initiative, meaning it is meant to complement other federal and NSF efforts around data infrastructure, workforce development, and multidisciplinary teams tackling major scientific, biomedical, and engineering challenges. In practical terms, NSF is looking for highly innovative projects that develop or rigorously evaluate new methods and technologies that make big data more usable, reliable, and impactful across many domains of science and engineering.

The solicitation invites two main types of proposals, and applicants are expected to choose the category that best matches their aims. The first category, Foundations (F), targets work on fundamental advances: new theories, methodologies, core techniques, and enabling technologies that have broad applicability across many big data problems rather than being tied to a single use case. This track is well-suited for research that strengthens the underlying scientific and engineering basis of big data, such as new algorithmic principles, statistical frameworks, mathematical models, or general-purpose systems approaches that could be reused across disciplines. The second category, Innovative Applications (IA), is for projects that still develop important methods and technologies, but do so in direct connection with a specific big data problem that impacts at least one clearly identified application area. In other words, IA proposals are expected to show how the technical innovation is motivated by, validated on, and beneficial to a particular domain challenge, rather than staying purely general.

Regardless of category, every proposal must directly address critical challenges in at least one of three emphasis areas: big data management, big data analytics, or scientific discovery processes that are affected by big data. The solicitation is intentionally open about the kinds of approaches that qualify, emphasizing that relevant techniques and technologies may be computational, statistical, or mathematical in nature. Projects may focus on novel theoretical analysis (for example, provable properties, complexity, guarantees, or new conceptual frameworks) and/or experimental evaluation (for example, prototype implementations, benchmarking, empirical studies, or validation in realistic environments). A consistent requirement across the solicitation is a high level of innovation, meaning incremental improvements or routine applications of existing methods are less likely to be competitive than genuinely new ideas, architectures, or rigorous advances that change what is possible with big data.

The opportunity is broad in disciplinary scope. NSF welcomes proposals spanning all areas of science and engineering covered by participating NSF directorates, including Engineering; Computer and Information Science and Engineering; Biological Sciences; Social, Behavioral, and Economic Sciences; and Education and Human Resources. This breadth signals that NSF is not only interested in computer science innovations in isolation, but also in advances that enable or transform data-intensive work in fields like biology, social science, and engineering, as long as the proposal meaningfully tackles big data challenges. For Innovative Applications (IA) proposals specifically, applicants must name one or more relevant participating NSF directorates in the Project Summary, which helps NSF route and review the work within the appropriate disciplinary and programmatic context.

NSF also points applicants to consider whether other, related solicitations might be a better fit before submitting. The solicitation explicitly encourages prospective applicants to review NSF’s list of related big data opportunities and to speak with the program officers associated with those programs if they appear more aligned. Two examples are highlighted. First, teams interested in deployable cyberinfrastructure pilot efforts intended to support a broader research community are directed toward the Data Infrastructure Building Blocks (DIBBS) solicitation, which focuses more on infrastructure building blocks and community-facing capabilities. Second, applicants are advised to consider the Computational and Data-Enabled Science and Engineering (CDSE) solicitations if their work aligns more naturally with that program’s scope. This guidance is important because NSF is signaling that BIGDATA 14-543 is not meant to cover every data-related project; it is specifically focused on critical techniques and technologies and expects a strong research contribution at that level.

From an administrative and funding perspective, the opportunity is categorized as discretionary funding, and the funding instrument type is a cooperative agreement. NSF anticipated approximately 30 awards under this solicitation, supported by an estimated total funding amount of $23,000,000. Typical award sizes were bounded by an award floor of $600,000 and an award ceiling of $2,000,000, and there was no cost sharing or matching requirement listed. Eligibility was described as unrestricted (open broadly to any type of entity), subject to any clarifications in the full text. Key dates included a posted date of March 12, 2014 and a full proposal deadline of June 9, 2014 (with an archive date of July 9, 2014), indicating this specific call was time-limited but provides a clear snapshot of NSF’s priorities and expectations for big data methods research during that cycle.

Overall, this solicitation is best understood as NSF’s push to fund research that materially advances the technical foundations and/or high-impact application-driven methods needed to manage and learn from big data, with strong emphasis on novelty, rigor (theoretical and/or experimental), and relevance to critical big data challenges. It encourages wide participation across NSF-supported disciplines while also steering applicants to alternative programs when the core goal is more about deployable infrastructure or other data-enabled science efforts rather than the development of fundamental or application-anchored big data techniques.

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